How do I securely obtain my API Key?

Installation in Pumble is unique. In your workspace, click **+ Add apps** in the left sidebar, find the **API** addon, and install it. After doing so, go to any chat input box in Pumble and type `/api-keys generate`. A private, ephemeral message will appear containing your secret token. Paste that token here.

Can my AI automatically reply to unread issues?

Yes. You can instruct your agent to regularly fetch messages from a specific channel (`chat_history_messages`). The agent can evaluate questions, generate answers based on your knowledge base, and then use `chat_post_message` to post the solution back to the team instantly.

Is it possible for the AI to react with emojis?

Absolutely. It uses the `chat_add_reaction` functionality. A common use case is having the agent process a batch of tickets reported in Pumble, and instruct it to leave a '✅' checkmark reaction on the original message to signify that work is complete.

What happens if a bot message has a typo?

Unlike standard webhooks, this integration provides full bidirectional control. If your AI posted something incorrect, simply ask it to update its previous message. It will fetch its message ID and push a new payload using the `chat_update_message` tool instantly without leaving double messages.

Pumble MCP Server for LangChain 10 tools — connect in under 2 minutes

Name: Pumble
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 10 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Pumble through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "pumble": {
            "transport": "streamable_http",
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
        }
    }) as client:
        tools = client.get_tools()
        agent = create_react_agent(
            ChatOpenAI(model="gpt-4o"),
            tools,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using Pumble, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Pumble MCP Server

Connect your Pumble workspace to any AI agent and bring powerful automation directly to your team's communication hub.

LangChain's ecosystem of 500+ components combines seamlessly with Pumble through native MCP adapters. Connect 10 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

What you can do

Read & Manage Channels — List all public and private channels, fetch detailed metadata, and dynamically create new discussion channels on the fly
Message Operations — Retrieve conversation histories, post new messages, update typos, or delete outdated announcements seamlessly
Interactive Reactions — Add emoji reactions to messages automatically to acknowledge requests without cluttering the chat
User Directory — List all workspace users and pull detailed profiles (including emails and time zones) to ensure accurate tagging

The Pumble MCP Server exposes 10 tools through the Vinkius. Connect it to LangChain in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Pumble to LangChain via MCP

Follow these steps to integrate the Pumble MCP Server with LangChain.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 10 tools from Pumble via MCP

Why Use LangChain with the Pumble MCP Server

LangChain provides unique advantages when paired with Pumble through the Model Context Protocol.

The largest ecosystem of integrations, chains, and agents. combine Pumble MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across Pumble queries for multi-turn workflows

Pumble + LangChain Use Cases

Practical scenarios where LangChain combined with the Pumble MCP Server delivers measurable value.

RAG with live data: combine Pumble tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query Pumble, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain Pumble tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every Pumble tool call, measure latency, and optimize your agent's performance

Pumble MCP Tools for LangChain (10)

These 10 tools become available when you connect Pumble to LangChain via MCP:

chat_add_reaction

Adds an emoji reaction to a message

chat_delete_message

This action is irreversible. Deletes a message from a Pumble channel

chat_history_messages

Retrieves recent messages from a channel

chat_post_message

Specify the channel ID and the message text. Sends a message to a Pumble channel

chat_update_message

Updates a pre-existing message

create_chat_channel

Specify name and whether it should be private. Creates a new communication channel

get_channel_info

Retrieves detailed information about a specific channel

get_user_info

Retrieves detailed information for a specific user

list_all_channels

Lists all public and private channels available in the workspace

list_workspace_users

Lists all users in the Pumble workspace

Example Prompts for Pumble in LangChain

Ready-to-use prompts you can give your LangChain agent to start working with Pumble immediately.

"List all our active channels in Pumble."

"Post a message in the #dev-updates channel stating that 'Deployment 2.1 is completed'."

"Read the last 3 messages from #marketing-q4 and react to the last one with a 'thumbsup'."

Troubleshooting Pumble MCP Server with LangChain

Common issues when connecting Pumble to LangChain through the Vinkius, and how to resolve them.

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Pumble + LangChain FAQ

Common questions about integrating Pumble MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.